def run_param_search(problem, output_dir):
rhc = mlrose.RHCRunner(problem=problem,
experiment_name="RHC",
output_directory=output_dir,
seed=42,
iteration_list=2**np.arange(15),
max_attempts=1000,
restart_list=[10])
rhc_run_stats, rhc_run_curves = rhc.run()
sa = mlrose.SARunner(problem=problem,
experiment_name="SA",
output_directory=output_dir,
seed=42,
iteration_list=2**np.arange(20),
max_attempts=1000,
temperature_list=[100, 250, 500],
decay_list=[mlrose.ExpDecay, mlrose.GeomDecay])
sa_run_stats, sa_run_curves = sa.run()
ga = mlrose.GARunner(problem=problem,
experiment_name="GA",
output_directory=output_dir,
seed=42,
iteration_list=2**np.arange(13),
max_attempts=1000,
population_sizes=[100, 200, 300],
mutation_rates=[0.1, 0.2, 0.3])
ga_run_stats, ga_run_curves = ga.run()
mimic = mlrose.MIMICRunner(problem=problem,
experiment_name="MIMIC",
output_directory=output_dir,
seed=42,
iteration_list=2**np.arange(13),
population_sizes=[100, 200, 300],
max_attempts=500,
keep_percent_list=[0.1, 0.2, 0.3],
use_fast_mimic=True)
mimic_run_stats, mimic_run_curves = mimic.run()
pN = [5, 10, 15, 30, 55, 100]
for i in pN:
problems_list.append((peaksGen.generate(RANDOM_STATE, i, t_pct), f'peaks_{i}'))
print('Building experiments...')
## Algorithms
experiments = []
for prob in problems_list: # Test if file already exists, if so skip
problem, expName = prob
iList = 2**np.arange(10)
# Random Hill Climbing
if not os.path.isfile(os.path.join(sys.path[0],'Output', expName, f'rhc__{expName}__curves_df.csv')):
rhc = mlr.RHCRunner(problem=problem,
experiment_name=expName,
output_directory=OUTPUT_DIRECTORY,
seed=RANDOM_STATE,
iteration_list=iList,
restart_list=[5,10,20,30],
verbose=False)
experiments.append(rhc)
# s,c = rhc.run()
# results.append([s,c])
# Simulated Annealing
if not os.path.isfile(os.path.join(sys.path[0],'Output', expName, f'sa__{expName}__curves_df.csv')):
sa = mlr.SARunner(problem=problem,
experiment_name=expName,
output_directory=OUTPUT_DIRECTORY,
seed=RANDOM_STATE,
iteration_list=iList,
temperature_list=[1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000],
decay_list=[mlr.ArithDecay, mlr.ExpDecay, mlr.GeomDecay],
max_attempts=max_attempts,
max_iters=max_iters,
random_state=random_state,
pop_size=pop_size,
keep_pct=keep_pct,
curve=True)
print("MIMIC - Total Function Evaluations:", eval_count)
plot_fitness_iteration('fitness_iteration_mimic_queens.png',mimic_curve,
"Queens - Mimic: Population Size: {}, Keep Percent: {}".format(pop_size, keep_pct))
# RHC
rhc = mlrh.RHCRunner(problem=prob,
experiment_name=experiment_name,
output_directory=output_directory,
seed=random_state,
max_attempts=200,
iteration_list=[2500],
restart_list=[20])
rhc_stats, rhc_curve = rhc.run()
columns = ['Time', 'Fitness', 'Restarts', 'current_restart']
df=pd.read_csv("./queen/queen_prob/rhc__queen_prob__run_stats_df.csv")
print(df[columns].sort_values(by=['Fitness'], ascending=False))
max_attempts = 500
max_iters = 2500
restarts = 20
eval_count = 0
best_state, best_fitness, rhc_curve = mlrh.random_hill_climb(prob,
max_attempts=max_attempts,